Biology subjects

Kaster, J.

Publications and source records attributed to Kaster, J..

5 recordsLinked to original sources

bioRxiv2026

Single-cell lineage tracing maps clonal and transcriptional dynamics in melanoma metastasis

Melanoma metastasis is driven by extensive intratumoral heterogeneity and phenotypic plasticity, yet how clonal identity relates to transcriptional programs during metastasis remains unclear. Here, we applied MeRLin, a single-cell lineage tracing platform, to dissect the clonal and transcriptional heterogeneity of metastatic melanoma in a patient-derived spontaneous metastasis model. Clonal analyses revealed hierarchical structures during tumor progression, with a subset of lineages from primary tumors consistently enriched across metastatic sites, supporting a model of polyclonal seeding followed by selective expansion of pre-existing highly metastatic subpopulations. Single-cell transcriptomic profiling identified two major metastatic subpopulations of distinct transcriptional programs, characterized by neural crest stem cell-like and lipid metabolism signatures. Both programs were enriched for invasion-associated genes and maintained across organs through distinct regulatory networks. Spatial mapping by barcode RNA-FISH linked these transcriptional states to their tissue context and showed that OLFML3 expression partially co-localized with a dominant subpopulation at the tumor-liver interface, marking the invasive fronts of metastatic growth. Together, these findings establish a framework in which clonal identity, transcriptional state, and spatial organization jointly shape metastatic melanoma progression.

cancer biology↗

bioRxiv2025

Clonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance

Most advanced melanomas initially respond to targeted therapy but eventually relapse. Rather than acquiring new mutations, resistance is driven by drug-tolerant persister cells that enter a reversible drug-refractory state. We developed MeRLin, a high-resolution lineage tracing platform integrating cellular barcoding, single-cell transcriptomics, RNA fluorescence in situ hybridization (FISH), and computational analyses to track clonal and transcriptional dynamics in patient-derived melanoma models during prolonged therapy. Clonal dynamics revealed that persister subpopulations first responded to treatment but persisted and expanded during minimal residual disease, ultimately leading to tumor recurrence. Pre-treatment melanoma populations diversified into four conserved persister states characterized by stress-like, lipid metabolism, PI3K signaling, and extracellular matrix remodeling programs associated with adaptive resistance. Spatial transcriptomics showed the organization of these adaptive programs and a complex signaling network of autocrine and paracrine interactions among persister subpopulations. Barcoded RNA-FISH enabled spatial mapping of clonal identity and gene expression, revealing in situ co-localization of a dominant resistant clone with SLC2A1 expression. MeRLin provides a robust framework for dissecting cancer heterogeneity and identifying vulnerabilities in persister populations.

cancer biology↗

bioRxiv2025

SpaceBar enables clone tracing in spatial transcriptomic data

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

genomics↗

bioRxiv2024

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

Resistance of BRAF-mutant melanomas to targeted therapy arises from the ability of cells to enter a persister state, evade treatment with relative dormancy, and repopulate the tumor when reactivated. Using spatial transcriptomics in patient derived xenograft models, we capture clonal lineage evolution during treatment, finding the persister state to show increased oxidative phosphorylation, decreased proliferation, and increased invasive capacity, with central-to-peripheral gradients. Phylogenetic tracing identifies intrinsic- and acquired-resistance mechanisms (e.g. dual specific phosphatases, Reticulon-4, CDK2) and suggests specific temporal windows of potential therapeutic efficacy. Using deep learning to analyze histopathological slides, we find morphological features of specific cell states, demonstrating that juxtaposition of transcriptomics and histology data enables identification of phenotypically-distinct populations using imaging data alone. In summary, we define state change and lineage selection during melanoma treatment with spatiotemporal resolution, elucidating how choice and timing of therapeutic agents will impact the ability to eradicate resistant clones. Statement of SignificanceTumor evolution is accelerated by application of anti-cancer therapy, resulting in clonal expansions leading to dormancy and subsequently resistance, but the dynamics of this process are incompletely understood. Tracking clonal progression during treatment, we identify conserved, global transcriptional changes and local clone-clone and spatial patterns underlying the emergence of resistance.

cancer biology↗

bioRxiv2023

Nextflow Pipeline for Visium and H&E Data from Patient-Derived Xenograft Samples

HighlightsWe have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework. SummaryWe designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.

bioinformatics↗